AI & APP Economies Taking Centre Stage As Indian Markets Fire Up Again
Puneet Gupta, Vice President & Managing Director, NetApp India/SAARC, 0
The COVID-19 pandemic accelerated the move towards a digital-led world. In India, we saw a massive transformation with consumers adopting digital lifestyles whether it is students switching to online school or corporate professionals adopting remote work. As they become more digital and embrace a mobile-first lifestyle, more and more ‘SuperApps’ are emerging apps that offer a gamut of services on a unified platform, will account for half of global consumption growth in the next decade, and will be a $10 trillion opportunity, as reported by McKinsey.
Disruptions are changing the landscape in more ways than we’ve ever imagined. Asian businesses are finding ways to detect any emerging signals of change, to be adaptive or agile to pivot, or even stay ahead of the curve in some cases. Across industries, data has become the greatest resource to support their digital centric imperatives. Data has become much easier to collect, store and mine, and to support this, we are seeing another new trend businesses are turning to the Cloud as the foundation for their business infrastructure.
Cloud Computing the Promise & the Challenge
IDC recently stated that the overall Indian public cloud services market is expected to reach $13.5 billion by 2026, growing at a CAGR of 24 percent during 2021- 26. The numbers are a clear indicator that Cloud will play a critical role in the future of most Enterprise Org. Businesses are seeing the benefits when data is free to move across public and private clouds to be accessed and utilized by the right staff, at the right time. Cloud reliability, scalability, availability and reduced CapEx spending empower enterprises to unlock new revenue streams and business opportunities.
However, the challenge is not simply a shift to the cloud. Just as there are enterprises that have successfully taken advantage of their cloud infrastructure, IDC also noted that more than 85 percent of APEJ organizations are struggling to cross the cloud maturity chasm and gain agility in cloud adoption. There are those who face infrastructure complexities and costs, struggling to enjoy the benefits of their cloud investments simplicity, agility and security or compliance requirements.
Can Artificial Intelligence(AI) be the Solution?
As with most cloud operations or migrations, bottleneck issues can happen when the data is required to traverse from the edge to the core, to the cloud, and back again, especially when they are involved in multiple or hybrid multi-cloud environments. Data tiering is a process many organisations apply to data management flagging data as either hot, cool, cold or frozen (i.e., how actively they are needed and utilised by users) and shifting from one storage tier to another as its state dynamically changes. With data sets and storage environments growing exponentially now, automated work flows and processes are a necessity. Increasingly, some organisations have also started to apply AI to assist in their Data Operations and Cloud Operations. The combination of AI and cloud computing allows for an extensive network that learns and improves continuously, while holding massive volumes of data right across the cloud, regardless of where they are stored. Machine learning algorithms train the AI to automate complex and repetitive tasks to boost productivity, perform data analysis with little human engagement, and manage and monitor core workflows. The AI performs the tedious, mundane tasks of data management, while the human IT teams focus more on strategic operations.
We have seen organizations that have been able to scale their operations using AI together with other cloud data management solutions, leading them to set new standards and drive innovation for themselves. For example, Narayana Health Group is India’s leading healthcare provider and one of the largest hospital groups in the country, with a network of 22 hospitals, 6 heart centers, and 19 primary care facilities. The healthcare group treats more than 2.6 million patients every year. They amass enormous amounts of unstructured data such as medical images and videos from hospitals and clinics. They then utilize deep learning algorithms to improve diagnostics, thereby helping the clinicians on their decision making. Having an increased data capacity meant that Narayana Health Group has not only ample space to store its data, but also the capacity to grow its datasets and help its machine learning algorithms achieve even faster training times.
Data is the New Gold
As India’s digital economy continues to grow, we’ll see more innovative, AI-integrated ways to manage and wield data. With the right automated management and migration tools, the hybrid multicloud approach clearly offers the utmost flexibility in managing data wherever it is, whenever you need it, at the best cost possible.
As with most cloud operations or migrations, bottleneck issues can happen when the data is required to traverse from the edge to the core, to the cloud, and back again, especially when they are involved in multiple or hybrid multi-cloud environments. Data tiering is a process many organisations apply to data management flagging data as either hot, cool, cold or frozen (i.e., how actively they are needed and utilised by users) and shifting from one storage tier to another as its state dynamically changes. With data sets and storage environments growing exponentially now, automated work flows and processes are a necessity. Increasingly, some organisations have also started to apply AI to assist in their Data Operations and Cloud Operations. The combination of AI and cloud computing allows for an extensive network that learns and improves continuously, while holding massive volumes of data right across the cloud, regardless of where they are stored. Machine learning algorithms train the AI to automate complex and repetitive tasks to boost productivity, perform data analysis with little human engagement, and manage and monitor core workflows. The AI performs the tedious, mundane tasks of data management, while the human IT teams focus more on strategic operations.
The combination of ai and cloud computing allows for an extensive network that learns and improves continuously, while holding massive volumes of data right across the cloud, regardless of where they are stored
We have seen organizations that have been able to scale their operations using AI together with other cloud data management solutions, leading them to set new standards and drive innovation for themselves. For example, Narayana Health Group is India’s leading healthcare provider and one of the largest hospital groups in the country, with a network of 22 hospitals, 6 heart centers, and 19 primary care facilities. The healthcare group treats more than 2.6 million patients every year. They amass enormous amounts of unstructured data such as medical images and videos from hospitals and clinics. They then utilize deep learning algorithms to improve diagnostics, thereby helping the clinicians on their decision making. Having an increased data capacity meant that Narayana Health Group has not only ample space to store its data, but also the capacity to grow its datasets and help its machine learning algorithms achieve even faster training times.
Data is the New Gold
As India’s digital economy continues to grow, we’ll see more innovative, AI-integrated ways to manage and wield data. With the right automated management and migration tools, the hybrid multicloud approach clearly offers the utmost flexibility in managing data wherever it is, whenever you need it, at the best cost possible.